VietMed-MCQ: A New Benchmark for Vietnamese Traditional Medicine

Large Language Models (LLMs) have taken the world by storm, showing prowess in general medical knowledge. Yet, when these models are tasked with niche subjects such as Vietnamese Traditional Medicine (VTM), their performance takes a nosedive. Why? The absence of high-quality, specialized benchmarks is the simple answer.

Introducing VietMed-MCQ

In response to this gap, a groundbreaking dataset called VietMed-MCQ has been developed. Comprising 3,190 questions, this dataset spans three difficulty levels and was rigorously validated by one medical expert and four students. The verdict? A 94.2 percent approval rate with substantial inter-rater agreement, boasting a Fleiss' kappa of 0.82.

VietMed-MCQ employs a Retrieval-Augmented Generation (RAG) pipeline with an automated consistency check, aiming to produce more reliable data. Unlike its synthetic predecessors, this dataset uses a dual-model validation to ensure reasoning consistency, though it's not without its flaws. The substring-based evidence checking does have known limitations, but it's a step in the right direction.

Benchmarking the Models

Seven open-source models were put to the test using VietMed-MCQ. Interestingly, models built with strong Chinese priors performed better than the Vietnamese-centric ones, shedding light on the potential for cross-lingual conceptual transfer. But let's not get ahead of ourselves. Despite these advances, all models struggled with complex diagnostic reasoning. Is this transfer of knowledge really as effective as it seems, or are we just scratching the surface?

Towards Better AI in Low-Resource Domains

The creators of VietMed-MCQ have made both the code and dataset publicly available, a commendable move to spur further research in low-resource medical domains. Yet, one can't help but question why it took this long for such efforts to materialize. The incentive for progress in specialized fields like VTM shouldn't rely solely on the benevolence of academic pioneers.

In the end, VietMed-MCQ is more than just a dataset. It's a call to action for the AI community to bridge the gaping chasm between general AI capabilities and specialized knowledge domains. The burden of proof sits with the team, not the community, to ensure that these tools can be as effective in culturally specific contexts as they're in general ones.